Results 71 to 80 of about 5,750 (118)

Identifiability of errors in variables dynamic systems

Automatica, 2006
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Juan C. Agüero, Graham C. Goodwin
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A Note on an 'Errors in Variables' Model

Journal of the American Statistical Association, 1966
Abstract We consider an errors in variables model in which the ‘true’ part of the determining variable is generated by a simple forecasting mechanism. It is shown that the Least Squares errors in variables bias can be interpreted in terms of the parameters of the forecasting mechanism; and that the ‘standard’ result for this bias may no longer hold in ...
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Identification of nonlinear errors-in-variables models

Automatica, 2002
The publication deals with a generalization of a classical eigenvalue-decomposition method first developed for errors-in-variables linear system identification. An identification algorithm is presented for nonlinear, but linear in parameters errors-in-variables models using nonlinear polynomial eigenvalue-eigenvector decompositions.
István Vajk, Jenö Hetthéssy
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Bootstrapping Errors-in-Variables Models

2000
The bootstrap is a numerical technique, with solid theoretical foundations, to obtain statistical measures about the quality of an estimate by using only the available data. Performance assessment through bootstrap provides the same or better accuracy than the traditional error propagation approach, most often without requiring complex analytical ...
Bogdan Matei, Peter Meer
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Identifiability in dynamic errors-in-variables models

The 22nd IEEE Conference on Decision and Control, 1983
Abstract. This paper is concerned with the identifiability of scalar linear dynamic errors‐in‐variables systems. The analysis is based on second moments only. The set of feasible systems corresponding to given second moments of the observations is described and conditions for identifiability are derived for the case of rational transfer functions.
Anderson, Brian D.O., Deistler, Manfred
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Identification in the Linear Errors in Variables Model

Econometrica, 1983
Consider the following multiple linear regression model with errors in variables: \(y_ j=\xi^ T\!_ j\beta +\epsilon_ j\), \(x_ j=\xi_ j+\nu_ j\), \(j=1,...,n\), where \(\xi_ j\), \(x_ j\), \(\nu_ j\), and \(\beta\) are k-vectors, \(y_ j\), \(\epsilon_ j\) are scalars. The \(\xi_ j\) are unobserved variables: instead the \(x_ j\) are observed.
Kapteyn, Arie, Wansbeek, Tom
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Boosted Regression Trees with Errors in Variables

Biometrics, 2007
Summary In this article, we consider nonparametric regression when covariates are measured with error. Estimation is performed using boosted regression trees, with the sum of the trees forming the estimate of the conditional expectation of the response. Both binary and continuous response regression are investigated.
Sexton, Joseph, Laake, Petter
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The Degenerate Bounded Errors-in-Variables Model

SIAM Journal on Matrix Analysis and Applications, 2001
The paper is devoted to a special case of the error-in-variable problem. It is viewed as total least squares with bounds on the uncertainty in the coefficient matrix. The chosen approach advantage is given as a motivation for further considerations. Corresponding proofs and algorithm synthesis are presented.
Chandrasekaran, S., Gu, M., Sayed, Ali H., Schubert, K. E.   +3 more
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The Variance of Nonparametric Errors- in-Variables Estimates

IEEE Transactions on Instrumentation and Measurement, 2004
Frequency response functions (FRFs) measured by taking the ratio of the output to the input Fourier coefficients of the steady-state response of the system to a periodic excitation are considered. Under assumptions of additive Gaussian noise on both the inputs and outputs, the variance of such measurements is infinite.
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Linear errors-in-variables models

1984
In this paper we are concerned with the statistical analysis of systems, where both, inputs and outputs, are contaminated by errors. Models of this kind are called error-in-variables (EV) models. Let x t * . and y t * denote the “true” inputs and outputs respectively and let xt and yt denote the observed inputs and outputs, then the situation can be ...
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